Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes

TL;DR

This study evaluates the effectiveness of AI-generated strategies to counteract gender stereotypes online, revealing which approaches are most robust and highlighting challenges in perception and offensiveness.

Contribution

It introduces and assesses eleven AI-driven counter-stereotype strategies, identifying the most effective methods and providing insights into their perception and impact.

Findings

Counter-facts and broadening universals are most effective strategies.

Many AI-generated counter-stereotypes are perceived as offensive or implausible.

Perceptions vary more across stereotype targets than between raters' genders.

Abstract

Gender stereotypes are pervasive beliefs about individuals based on their gender that play a significant role in shaping societal attitudes, behaviours, and even opportunities. Recognizing the negative implications of gender stereotypes, particularly in online communications, this study investigates eleven strategies to automatically counter-act and challenge these views. We present AI-generated gender-based counter-stereotypes to (self-identified) male and female study participants and ask them to assess their offensiveness, plausibility, and potential effectiveness. The strategies of counter-facts and broadening universals (i.e., stating that anyone can have a trait regardless of group membership) emerged as the most robust approaches, while humour, perspective-taking, counter-examples, and empathy for the speaker were perceived as less effective. Also, the differences in ratings were more pronounced for stereotypes about the different targets than between the genders of the raters. Alarmingly, many AI-generated counter-stereotypes were perceived as offensive and/or implausible. Our analysis and the collected dataset offer foundational insight into counter-stereotype generation, guiding future efforts to develop strategies that effectively challenge gender stereotypes in online interactions.